Yazar "Das, Sanchoy K." seçeneğine göre listele

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    Evaluating Supply Chain Flexibility with Order Quantity Constraints and Lost Sales
    (Elsevier Science Bv, 2010) Kesen, Saadettin Erhan; Kanchanapiboon, Atipol; Das, Sanchoy K.
    In a flexible supply chain buyers and suppliers are willing to accommodate the uncertainties and variations in each other's businesses. In many instances the buyer may prefer to use supply flexibility, as opposed to an inventory holding strategy, to counter demand uncertainty. We consider the case where the buyer releases a fixed period replenishment order to the supplier under a supply contract defined by three parameters: (i) supply price per unit (ii) minimum order quantity and (iii) order quantity reduction penalty. Following a demand drop the buyer therefore has two flexibility options in the order cycle: (i) to place an order less than the supplier specified minimum order quantity and pay the associated penalty, or (ii) place no order and lose the sales for the current period. There is no penalty for not placing an order. A key buyer decision then is Q(lost), the order or replenishment quantity level below which no order is placed and the sales are lost. A model for deriving the expected supply and lost sales cost as a function of Q(lost) is presented, and it is shown that the optimal value of Q(lost) is the inflexion point of the lost sales cost and the quantity penalty. The model is then used to select the supplier that minimizes the procurement plus lost sales costs from a given set of supply bids and a known expected customer demand behavior. Finally, the buyer also has the option to make capital investments in the supplier so as to reduce the minimum order quantity and hence reduce the projected supply costs. We evaluate the economics of this tactic.
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    A Genetic Algorithm Based Heuristic for Scheduling of Virtual Manufacturing Cells (VMCs)
    (Pergamon-Elsevier Science Ltd, 2010) Kesen, Saadettin Erhan; Das, Sanchoy K.; Güngör, Zülal
    We present a genetic algorithm (GA) based heuristic approach for job scheduling in virtual manufacturing cells (VMCs). In a VMC, machines are dedicated to a part as in a regular cell, but machines are not physically relocated in a contiguous area. Cell configurations are therefore temporary, and assignments are made to optimize the scheduling objective under changing demand conditions. We consider the case where there are multiple jobs with different processing routes. There are multiple machine types with several identical machines in each type and are located in different locations in the shop floor. Scheduling objective is weighted makespan and total traveling distance minimization. The scheduling decisions are the (i) assignment of jobs to the machines, and (ii) the job start time at each machine. To evaluate the effectiveness of the GA heuristic we compare it with a mixed integer programming (MIP) solution. This is done on a wide range of benchmark problem. Computational results show that GA is promising in finding good solutions in very shorter times and can be substituted in the place of MIP model.
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    A Mixed Integer Programming Formulation for Scheduling of Virtual Manufacturing Cells (VMCs)
    (Springer London Ltd, 2010) Kesen, Saadettin Erhan; Das, Sanchoy K.; Güngör, Zülal
    We present a multi-objective mixed integer programming formulation for job scheduling in virtual manufacturing cells (VMCs). In a VMC, machines are dedicated to a part family as in a regular cell, but machines are not physically relocated in a contiguous area. Cell configurations are therefore temporary, and assignments are made to optimize the scheduling objective under changing demand conditions. We consider the case where there are multiple jobs with different processing routes. There are multiple machine types with several identical machines in each type and are located in different locations in the shop floor. The two scheduling objectives are makespan minimization and minimizing total traveling distance. Since batch splitting is permitted in the system, scheduling decisions must tell us the (a) assignment of jobs to the machines, (b) the job starting time at each machine, and (c) the part quantity processed on different machines due to batch splitting. Under these decision variables, the objective function is to minimize the sum of the makespan and total traveling distance/cost. Illustrative examples are given to demonstrate the implementation of the model.